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Health applications for climate data. Pollen Grains. Shubhayu Saha Climate and Health Program Centers for Disease Control and Prevention. CDC, National Center for Environmental Health. - PowerPoint PPT Presentation
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CDC, National Center for Environmental Health
Health applications for climate data
Shubhayu Saha
Climate and Health ProgramCenters for Disease Control and Prevention
Pollen Grains
CDC, National Center for Environmental Health
Presenter Disclosures
"The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official view of Centers for Disease Control
Shubhayu Saha
CDC, National Center for Environmental Health
Outline
Climate-sensitive health outcomes
CDC’s role in translation and capacity building
Example of establishing health-weather associations
Projecting future health burden
CDC, National Center for Environmental Health
National Climate Assessment –
Health implications
Temperature extremesAeroallergensVectorborne diseaseInjuries from extreme weather eventsWildfire
CDC, National Center for Environmental Health
Andersen and Bell, 2011, Environmental Health Perspectives
Mortality risk from heat waves
CDC, National Center for Environmental Health
Ziska et al., 2011 PNAS
Temperature increase and change in length of Ragweed season
Vectorborne diseases
Changes in georgaphical distributionLonger transmission seasonHigher tick densities
17029 cases
24364 cases
CDC, National Center for Environmental Health
Weather-related motor vehicle fatalities (Marmor et al, JAPH 2006)
CDC, National Center for Environmental Health
1. Forecasting Climate Impacts and Assessing Vulnerabilities
2. Projecting the Disease Burden
3. Assessing Public Health Interventions
4. Developing and Implementing a
Climate and Health Adaptation
Plan
5. Evaluating Impact and
Improving Quality of Activities
BuildingResilience
AgainstClimateEffects
Climate and Health Program, National Center for Environmental Health
CDC, National Center for Environmental Health
Generating County-level Measures
Population weightedCounty centroidCounty boundary
Geometric centroid of census blocks
Step 1: Creating population
weighted county centroid
Grid cell containing the population weighted centroid
NLDAS grid
Step 2: Selecting the grid cell
that contains the
populationweighted county
centroidStep 3: County-level values obtained by averaging values of all the 9 grid
cells
Adjacent grid cells
Daily Comparison: Scatter plot by Climate Region
Comparison for May – September 2006
NLD
AS-b
ased
max
imum
tem
pera
ture
(F)
Station-based maximum temperature (F)
r = 0.91 t = 0.76
r = 0.88 t = 0.69
r = 0.92 t = 0.76
r = 0.87t = 0.70
r = 0.87 t = 0.70
r = 0.90 t = 0.72
r = 0.90 t = 0.75
r = 0.82 t = 0.64
r = 0.89 t = 0.71
CDC, National Center for Environmental Health
The National Environmental Public Health Tracking Network
The network provides data on:
Extreme heat days and events Heat vulnerability Health effects associated with extreme heat
http://ephtracking.cdc.gov/showHome.action
CDC, National Center for Environmental Health
The National Environmental Public Health Tracking Network
http://ephtracking.cdc.gov/showHome.action
CDC, National Center for Environmental Health
What is the temporal association of Hyperthermia-related ED visit with different measures of ambient heat?
How does this association vary by place?
CDC, National Center for Environmental Health
Data elements
For 141 Metropolitan Statistical Areas in continental US:
National Climatic Data Center: Daily temperature, humidity 30 year daily normal for maximum temperature
Spatial Synoptic classification
MarketScan health data: ED visit of Hyperthermia by date, county of healthcare, age, gender
Air pollution data: Daily monitor-level PM2.5 and Ozone data
CDC, National Center for Environmental Health
Analytical strategy
1 8 15 22 31
Calendar month
Patient 1
Patient 2
Patient 3
Case crossover design – same patient treated as Case and Control
Half-month time-stratified control selection
Case day Control day
CDC, National Center for Environmental Health
Is the temperature different leading to an ED visit?
CDC, National Center for Environmental Health
A B C D
Cases 11270 11270 3828 3828
Control days 14070 14070 4738 4738
Maximum temperature oF 1.15 (1.14-1.15) 1.15 (1.14-1.15) 1.15 (1.14-1.17) 1.16 (1.15-1.17)
Heat wave indicator1 1.12 (0.96-1.30) 1.12 (0.83-1.52) 1.13 (0.84-1.54)
PM 2.5 concentration (mg/m3) 1.02 (1.01-1.03)
Ozone concentration (ppm) 1.00 (1.00-1.00)
Holiday indicator2 1.24 (0.82-1.88)
Model AIC 15163 15163 5071 5083
Conditional logistic regression
CDC, National Center for Environmental Health
<30
<=30
& <
32
<=32
& <
34
<=34
& <
36
<=36
& <
38
<=38
& <
40
<=40
& <
42
>= 4
2
82.00
84.00
86.00
88.00
90.00
92.00
94.00
0.00
0.20
0.40
0.60
0.80
1.00
1.20
Max TempZ scoreLatitude category oN
Max
imum
tem
pera
ture
on
day
of E
D vi
sit
Z-sc
ore
of M
ax T
emp
on d
ay o
f ED
visi
t
Temperature profile on ED visit days change by place
CDC, National Center for Environmental Health
Odds ratio of ED visit associated with extreme heat by Latitude
<30
<=30
& <
32
<=32
& <
34
<=34
& <
36
<=36
& <
38
<=38
& <
40
<=40
& <
42
>= 4
21.06
1.08
1.10
1.12
1.14
1.16
1.18
1.20
Latitude category oN
Odd
s Rati
o
5 11 23 13 8 7 15 12
CDC, National Center for Environmental Health
Random Effects meta-analysis of Odds Ratios of ED visit
Central
1.17 (1.16, 1.19)
West
1.12 (1.09, 1.14)
South
1.12 (1.10, 1.13)
West North Central
1.16 (1.07, 1.26)
East North Central
1.18 (1.14, 1.21)
Northwest
1.16 (1.07, 1.26)
Southwest
1.05 (1.02, 1.10)
Southeast
1.14 (1.12, 1.16)
Northeast
1.15 (1.13, 1.17)
2
8
1
4
25
8
22
13
11
CDC, National Center for Environmental Health
Benefit Mapping and Analysis tool EPA (Neal Fann, ISEE 2009)
CDC, National Center for Environmental Health
Study Location Temperature exposure Climate model Downscaling
Jackson et al. 2010 WA state Humidex HadCM (A1B), PCM1 (B1) x
Hayhoe et al., 2010 Chicago Spatial Synoptic Classification
GFDLCM2.1, HadCM3, PCM Statistical
Knowlton et al., 2007 NYC Mean Temperature GISS-MM5 Dynamic
Hayhoe et al., 2004 LA Maximum Apparent Temperature PCM, HadCM3 Statistical
Kalkstein et al., 1997 44 US cities Spatial Synoptic Classification
GFDL, UKMO, Max Planck model x
(Environmental Health Perspective, 2011)
CDC, National Center for Environmental Health
Li, Horton, Kinney (Nature, 2013)
CDC, National Center for Environmental Health
• Extreme weather events and their health impacts• Short vs long term decision-making horizon in public
health• Small spatial scale, as many health vulnerabilities are
highly localized
• Need for translating climate projections to an interested but uninformed health community
• Building regional collaborations
Conclusion